x̄ - > Packages and functions that allow you to perform a wide range of statistical tasks in R

1. Descriptive Statistics:

   - Calculate measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation) using functions like `mean()`, `median()`, `var()`, `sd()`, etc.

   - Generate summary statistics with `summary()` or `describe()`.

2. Inferential Statistics:

   - Conduct t-tests to compare means between groups using functions like `t.test()`.

   - Perform analysis of variance (ANOVA) using `aov()` or regression analysis with `lm()`.

   - Conduct chi-square tests for categorical data using functions like `chisq.test()`.

3. Correlation and Regression:

   - Calculate correlation coefficients using `cor()` or `cor.test()`.

   - Perform simple and multiple linear regression with `lm()` and examine model fit with `summary()`.

4. Hypothesis Testing:

   - Conduct hypothesis tests using functions such as `t.test()`, `prop.test()`, `wilcox.test()`, etc., depending on the nature of your data.

5. Time Series Analysis:

   - Analyze time series data using functions like `ts()`, `acf()`, `pacf()`, `arima()`, etc.

   - Perform forecasting using methods like ARIMA, exponential smoothing, or seasonal decomposition.

6. Data Visualization:

   - Create various types of plots, including histograms, scatter plots, box plots, bar plots, etc., using packages like `ggplot2`, `base`, or `lattice`.